Composition and structure of Pd nanoclusters in SiOx_x thin film

The nucleation, distribution, composition and structure of Pd nanocrystals in SiO$_2$ multilayers containing Ge, Si, and Pd are studied using High Resolution Transmission Electron Microscopy (HRTEM) and X-ray Photoelectron Spectroscopy (XPS), before and after heat treatment. The Pd nanocrystals in the as deposited sample seem to be capped by a layer of PdO$_x$. A 1-2 eV shift in binding energy was found for the Pd-3d XPS peak, due to initial state Pd to O charge transfer in this layer. The heat treatment results in a decomposition of PdO and Pd into pure Pd nanocrystals and SiO$_2$

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions

What Happens If an Unbroken Flavor Symmetry Exists?

Without assuming any specific flavor symmetry and/or any specific mass matrix forms, it is demonstrated that if a flavor symmetry (a discrete symmetry, a U(1) symmetry, and so on) exists, we cannot obtain the CKM quark mixing matrix $V$ and the MNS lepton mixing matrix $U$ except for those between two families for the case with the completely undegenerated fermion masses, so that we can never give the observed CKM and MNS mixings. Only in the limit of $m_{\nu 1} =m_{\nu 2}$ ($m_d=m_s$), we can obtain three family mixing with an interesting constraint $U_{e3}=0$ ($V_{ub}=0$).Comment: 10 pages, no figure, title and presentation change